class_docstr: Featurize messages using SpaCy.
docstr: The component's default config (see parent class for full docstring).
docstr_tok: ["The","component","'s","default","config","(","see","parent","class","for","full","docstring",")","."]
code: def get_default_config() -> Dict[Text, Any]:
      """The component's default config (see parent class for full docstring)."""
      return {
          **DenseFeaturizer.get_default_config(),
          # Specify what pooling operation should be used to calculate the vector of
          # the complete utterance. Available options: 'mean' and 'max'
          POOLING: MEAN_POOLING,
      }
  
imports: import numpy
  import typing
  import logging
  from typing import Any, Text, Dict, List, Type
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.graph import ExecutionContext, GraphComponent
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.nlu.featurizers.dense_featurizer.dense_featurizer import DenseFeaturizer
  from rasa.nlu.tokenizers.spacy_tokenizer import SpacyTokenizer
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.features import Features
  from rasa.shared.nlu.training_data.message import Message
  from rasa.nlu.constants import SPACY_DOCS, DENSE_FEATURIZABLE_ATTRIBUTES, FEATURIZER_CLASS_ALIAS
  from rasa.shared.nlu.constants import TEXT, FEATURE_TYPE_SENTENCE, FEATURE_TYPE_SEQUENCE
  from rasa.utils.tensorflow.constants import POOLING, MEAN_POOLING
label_desc: Use image node_id 3 for calling the SpacyFeaturizer obj's underlying member method code with example usage: obj.get_default_config() and returns: dict